This paper presents a new algorithm for computing visual correspondence with occlusions using graph cuts. The method addresses the issue of occlusions, which are a major challenge in accurate visual correspondence computation. Existing graph cut algorithms do not properly handle occlusions, as they treat the two input images asymmetrically and do not ensure that a pixel corresponds to at most one pixel in the other image. The proposed method ensures uniqueness, where each pixel is involved in at most one active assignment, and correctly identifies occluded pixels. The algorithm is based on energy minimization, where the energy function is designed to enforce uniqueness. The energy function consists of three terms: a data term that results from intensity differences between corresponding pixels, an occlusion term that imposes a penalty for making a pixel occluded, and a smoothness term that encourages neighboring pixels to have similar disparities. The smoothness term is chosen to allow efficient computation using graph cuts. The algorithm uses graph cuts to find a strong local minimum of the energy function. The graph is constructed such that each assignment corresponds to a vertex, and edges represent relationships between assignments. The minimum cut of this graph corresponds to the configuration that minimizes the energy function while ensuring uniqueness. The method is tested on stereo and motion tasks, and the results show that it performs well in detecting occlusions and computing disparities. The algorithm is efficient and can be applied to both stereo and motion problems. The results demonstrate that the method outperforms existing approaches in handling occlusions and provides accurate disparity maps.This paper presents a new algorithm for computing visual correspondence with occlusions using graph cuts. The method addresses the issue of occlusions, which are a major challenge in accurate visual correspondence computation. Existing graph cut algorithms do not properly handle occlusions, as they treat the two input images asymmetrically and do not ensure that a pixel corresponds to at most one pixel in the other image. The proposed method ensures uniqueness, where each pixel is involved in at most one active assignment, and correctly identifies occluded pixels. The algorithm is based on energy minimization, where the energy function is designed to enforce uniqueness. The energy function consists of three terms: a data term that results from intensity differences between corresponding pixels, an occlusion term that imposes a penalty for making a pixel occluded, and a smoothness term that encourages neighboring pixels to have similar disparities. The smoothness term is chosen to allow efficient computation using graph cuts. The algorithm uses graph cuts to find a strong local minimum of the energy function. The graph is constructed such that each assignment corresponds to a vertex, and edges represent relationships between assignments. The minimum cut of this graph corresponds to the configuration that minimizes the energy function while ensuring uniqueness. The method is tested on stereo and motion tasks, and the results show that it performs well in detecting occlusions and computing disparities. The algorithm is efficient and can be applied to both stereo and motion problems. The results demonstrate that the method outperforms existing approaches in handling occlusions and provides accurate disparity maps.